A New Mechanism for Job Scheduling in Computational Grid Network Environments

Consumption of energy in the large computing system is an important issue not only because energy sources are depleting fast but also due to the deteriorating environmental conditions. A computational grid is a large heterogeneous distributed computing platform which consumes enormous energy in the task execution. Energy-aware job scheduling, in the computational grid, is an important issue that has been addressed in this work. If the tasks are properly scheduled, keeping the optimal energy concern, it is possible to save the energy consumed by the system in the task execution. The prime objective, in this work, is to schedule the dependent tasks of a job, on the grid nodes with optimal energy consumption. Energy consumption is estimated with the help of Dynamic Voltage Frequency Scaling (DVFS). Makespan, while optimizing the energy consumption, is also taken care of in the proposed model. GA is applied for the purpose and therefore the model is named as Energy Aware Genetic Algorithm (EAGA). Performance evaluation of the proposed model is done using GridSim simulator. A comparative study with other existing models viz. min-min and max-min proves the efficacy of the proposed model.

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Balanced Job Scheduling Based on Ant Algorithm for Grid Network

International Journal of Grid and High Performance Computing ◽

10.4018/jghpc.2010092803 ◽

2010 ◽

Vol 2 (1) ◽

pp. 34-50 ◽

Cited By ~ 1

Author(s):

Nikolaos Preve

Keyword(s):

Grid Computing ◽

Ant Colony Optimization ◽

Job Scheduling ◽

Scheduling Algorithm ◽

Scheduling Algorithms ◽

Experimental Results ◽

Ant Algorithm ◽

Entire System ◽

Grid Environment ◽

Grid Network

Job scheduling in grid computing is a very important problem. To utilize grids efficiently, we need a good job scheduling algorithm to assign jobs to resources in grids. The main scope of this article is to propose a new Ant Colony Optimization (ACO) algorithm for balanced job scheduling in the Grid environment. To achieve the above goal, we will indicate a way to balance the entire system load while minimizing the makespan of a given set of jobs. Based on the experimental results, the proposed algorithm confidently demonstrates its practicability and competitiveness compared with other job scheduling algorithms.

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Efficient Job Scheduling in Computational Grid Systems Using Wind Driven Optimization Technique

International Journal of Applied Metaheuristic Computing ◽

10.4018/ijamc.2018010104 ◽

2018 ◽

Vol 9 (1) ◽

pp. 49-59

Author(s):

Tarun Kumar Ghosh ◽

Sanjoy Das

Keyword(s):

Job Scheduling ◽

Scheduling Algorithm ◽

Optimization Technique ◽

Computational Grid ◽

Grid System ◽

Swarm Optimization ◽

Grid Systems ◽

Geographically Distributed ◽

Job Scheduler ◽

Computational Resources

Computational Grid has been employed for solving complex and large computation-intensive problems with the help of geographically distributed, heterogeneous and dynamic resources. Job scheduling is a vital and challenging function of a computational Grid system. Job scheduler has to deal with many heterogeneous computational resources and to take decisions concerning the dynamic, efficient and effective execution of jobs. Optimization of the Grid performance is directly related with the efficiency of scheduling algorithm. To evaluate the efficiency of a scheduling algorithm, different parameters can be used, the most important of which are makespan and flowtime. In this paper, a very recent evolutionary heuristic algorithm known as Wind Driven Optimization (WDO) is used for efficiently allocating jobs to resources in a computational Grid system so that makespan and flowtime are minimized. In order to measure the efficacy of WDO, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are considered for comparison. This study proves that WDO produces best results.

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